{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   pregnants                     768 non-null    int64  \n",
      " 1   Plasma_glucose_concentration  768 non-null    int64  \n",
      " 2   blood_pressure                768 non-null    int64  \n",
      " 3   Triceps_skin_fold_thickness   768 non-null    int64  \n",
      " 4   serum_insulin                 768 non-null    int64  \n",
      " 5   BMI                           768 non-null    float64\n",
      " 6   Diabetes_pedigree_function    768 non-null    float64\n",
      " 7   Age                           768 non-null    int64  \n",
      " 8   Target                        768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(r'D:\\CSDN_HOMEWORK\\fifth_week_work\\pima-indians-diabetes.csv')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22bc13ae550>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAPHElEQVR4nO3df6yeZX3H8ffHVkSn8vOA2JbVaLPInCLrGBmJ2cQYYNM2BpxGR+e6dFnYpnPZxn4Ef8wlmrkxJcbYDaWYTSU6BJ3RsYJTswkeFPk5RyUKJ+1otYAwdAb23R/P1YuH9gBPtfd5Dj3vV/Lkua/rvp77fE9ycj657vt+rjtVhSRJAE+adgGSpMXDUJAkdYaCJKkzFCRJnaEgSeqWT7uAH8fRRx9dq1evnnYZkvSEct11132nqmbm2/eEDoXVq1czOzs77TIk6QklybcfbZ+njyRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpG7QUEjyrSQ3Jrk+yWzrOzLJlUlua+9HtP4keW+SbUluSHLSkLVJkva1EDOFX6qqE6tqbWufB2ytqjXA1tYGOANY016bgPcvQG2SpDHTOH20DtjStrcA68f6L6mRLwOHJzluCvVJ0pI19DeaC/iXJAV8oKo2A8dW1Q6AqtqR5Jg2dgVw59hn51rfjvEDJtnEaCbB8ccf/2MX+LN/eMmPfQwdfK77q3OmXYI0FUOHwqlVtb39478yyX8+xtjM07fPY+FasGwGWLt2rY+Nk6QDaNDTR1W1vb3vBC4DTgbu2nNaqL3vbMPngFVjH18JbB+yPknSIw0WCkl+Iskz9mwDLwduAq4ANrRhG4DL2/YVwDntLqRTgHv3nGaSJC2MIU8fHQtclmTPz/nHqvpskq8AlybZCNwBnN3GfwY4E9gGPAC8YcDaJEnzGCwUqup24EXz9H8XOG2e/gLOHaoeSdLj8xvNkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUDR4KSZYl+VqST7f2c5Jck+S2JB9Lckjrf0prb2v7Vw9dmyTpkRZipvBG4Nax9ruAC6pqDXA3sLH1bwTurqrnARe0cZKkBTRoKCRZCfwy8PetHeClwMfbkC3A+ra9rrVp+09r4yVJC2TomcLfAn8E/F9rHwXcU1UPtvYcsKJtrwDuBGj7723jHyHJpiSzSWZ37do1ZO2StOQMFgpJfgXYWVXXjXfPM7Qm2PdwR9XmqlpbVWtnZmYOQKWSpD2WD3jsU4FXJjkTOBR4JqOZw+FJlrfZwEpgexs/B6wC5pIsBw4Ddg9YnyRpL4PNFKrqT6pqZVWtBl4DXFVVrwOuBs5qwzYAl7ftK1qbtv+qqtpnpiBJGs40vqfwx8Cbk2xjdM3gotZ/EXBU638zcN4UapOkJW3I00ddVX0e+Hzbvh04eZ4xPwDOXoh6JEnz8xvNkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUDRYKSQ5Ncm2Srye5OcnbWv9zklyT5LYkH0tySOt/Smtva/tXD1WbJGl+Q84U/hd4aVW9CDgROD3JKcC7gAuqag1wN7Cxjd8I3F1VzwMuaOMkSQtosFCokftb88ntVcBLgY+3/i3A+ra9rrVp+09LkqHqkyTta9BrCkmWJbke2AlcCXwTuKeqHmxD5oAVbXsFcCdA238vcNSQ9UmSHmnQUKiqh6rqRGAlcDLw/PmGtff5ZgW1d0eSTUlmk8zu2rXrwBUrSVqYu4+q6h7g88ApwOFJlrddK4HtbXsOWAXQ9h8G7J7nWJuram1VrZ2ZmRm6dElaUoa8+2gmyeFt+6nAy4BbgauBs9qwDcDlbfuK1qbtv6qq9pkpSJKGs/zxh/zIjgO2JFnGKHwurapPJ7kF+GiSdwBfAy5q4y8CPpxkG6MZwmsGrE2SNI+JQiHJ1qo67fH6xlXVDcCL5+m/ndH1hb37fwCcPUk9kqRhPGYoJDkUeBpwdJIjePhi8DOBZw9cmyRpgT3eTOG3gDcxCoDreDgUvge8b8C6JElT8JihUFXvAd6T5Her6sIFqkmSNCUTXVOoqguT/AKwevwzVXXJQHVJkqZg0gvNHwaeC1wPPNS6CzAUJOkgMuktqWuBE/zegCQd3Cb98tpNwLOGLESSNH2TzhSOBm5Jci2jJbEBqKpXDlKVJGkqJg2Ftw5ZhKR93fH2n5l2CVqEjj//xkGPP+ndR/82aBWSpEVh0ruP7uPhZawPYfTAnP+pqmcOVZgkaeFNOlN4xng7yXrmWb9IkvTE9iMtnV1Vn2T0WE1J0kFk0tNHrxprPonR9xb8zoIkHWQmvfvoFWPbDwLfAtYd8GokSVM16TWFNwxdiCRp+ia6ppBkZZLLkuxMcleSTyRZOXRxkqSFNemF5g8xeobys4EVwKdanyTpIDJpKMxU1Yeq6sH2uhiYGbAuSdIUTBoK30ny+iTL2uv1wHeHLEyStPAmDYXfAF4N/DewAzgL8OKzJB1kJr0l9S+ADVV1N0CSI4F3MwoLSdJBYtKZwgv3BAJAVe0GXjxMSZKkaZk0FJ6U5Ig9jTZTmHSWIUl6gpj0H/tfA/+e5OOMlrd4NfCXg1UlSZqKSb/RfEmSWUaL4AV4VVXdMmhlkqQFN/EpoBYCBoEkHcR+pKWzJUkHJ0NBktQZCpKkzlCQJHWGgiSpMxQkSd1goZBkVZKrk9ya5OYkb2z9Rya5Mslt7f2I1p8k702yLckNSU4aqjZJ0vyGnCk8CPxBVT0fOAU4N8kJwHnA1qpaA2xtbYAzgDXttQl4/4C1SZLmMVgoVNWOqvpq274PuJXRU9vWAVvasC3A+ra9DrikRr4MHJ7kuKHqkyTta0GuKSRZzWhV1WuAY6tqB4yCAzimDVsB3Dn2sbnWt/exNiWZTTK7a9euIcuWpCVn8FBI8nTgE8Cbqup7jzV0nr7ap6Nqc1Wtraq1MzM+EVSSDqRBQyHJkxkFwj9U1T+17rv2nBZq7ztb/xywauzjK4HtQ9YnSXqkIe8+CnARcGtV/c3YriuADW17A3D5WP857S6kU4B795xmkiQtjCEflHMq8GvAjUmub31/CrwTuDTJRuAO4Oy27zPAmcA24AF8BrQkLbjBQqGqvsT81wkATptnfAHnDlWPJOnx+Y1mSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqBguFJB9MsjPJTWN9Rya5Mslt7f2I1p8k702yLckNSU4aqi5J0qMbcqZwMXD6Xn3nAVurag2wtbUBzgDWtNcm4P0D1iVJehSDhUJVfQHYvVf3OmBL294CrB/rv6RGvgwcnuS4oWqTJM1voa8pHFtVOwDa+zGtfwVw59i4uda3jySbkswmmd21a9egxUrSUrNYLjRnnr6ab2BVba6qtVW1dmZmZuCyJGlpWehQuGvPaaH2vrP1zwGrxsatBLYvcG2StOQtdChcAWxo2xuAy8f6z2l3IZ0C3LvnNJMkaeEsH+rAST4C/CJwdJI54C3AO4FLk2wE7gDObsM/A5wJbAMeAN4wVF2SpEc3WChU1WsfZddp84wt4NyhapEkTWaxXGiWJC0ChoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWLKhSSnJ7kG0m2JTlv2vVI0lKzaEIhyTLgfcAZwAnAa5OcMN2qJGlpWTShAJwMbKuq26vqh8BHgXVTrkmSlpTl0y5gzArgzrH2HPDzew9KsgnY1Jr3J/nGAtS2VBwNfGfaRSwGefeGaZegR/Jvc4+35EAc5ScfbcdiCoX5ftPap6NqM7B5+HKWniSzVbV22nVIe/Nvc+EsptNHc8CqsfZKYPuUapGkJWkxhcJXgDVJnpPkEOA1wBVTrkmSlpRFc/qoqh5M8jvA54BlwAer6uYpl7XUeFpOi5V/mwskVfuctpckLVGL6fSRJGnKDAVJUmcoyOVFtGgl+WCSnUlumnYtS4WhsMS5vIgWuYuB06ddxFJiKMjlRbRoVdUXgN3TrmMpMRQ03/IiK6ZUi6QpMxQ00fIikpYGQ0EuLyKpMxTk8iKSOkNhiauqB4E9y4vcClzq8iJaLJJ8BPgP4KeSzCXZOO2aDnYucyFJ6pwpSJI6Q0GS1BkKkqTOUJAkdYaCJKlbNE9ekxaTJEcBW1vzWcBDwK7WPrmtE3Wgf+ZJwDFV9dkDfWxpUoaCNI+q+i5wIkCStwL3V9W7J/18kmVV9dB+/tiTgBcAhoKmxtNH0n5K8qkk1yW5Oclvtr7lSe5J8o4k1wInJ3lle07FF5NcmOSTbezTk1yc5NokX0vyiiRPBc4HXpfk+iRnTfFX1BLmTEHafxuqaneSpwGzST4B3AccBny1qv687fsv4FTgDuDSsc+fD3y2qn49yRHANcALgbcDL6iqNy3kLyONc6Yg7b/fT/J1RssvrASe2/p/CFzWtk8AvlFV367RsgEfGfv8y4E/S3I9cDVwKHD8glQuPQ5nCtJ+SPIy4CXAKVX1/SRfYvRPHeD79fC6MfMtSc7YvvVV9c29jv2SA16wtJ+cKUj75zBgdwuEnwZ+7lHG3cxoEbdVSQL86ti+zwG/t6eR5MVt8z7gGQPULE3MUJD2zz8DT2unj85ndD1gH1X1AKPVZ/8V+CKjZ1Tc23a/rR3jxiQ3A29t/VcBL2oXn73QrKlwlVRpIEmeXlX3t5nCB4Abq+rCadclPRZnCtJwfrtdTL4FeCrwd1OuR3pczhQkSZ0zBUlSZyhIkjpDQZLUGQqSpM5QkCR1/w8VpZuxbWRW0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Target',data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22bc123bb00>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'pregnants',data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22bc18527b8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEGCAYAAACD7ClEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deZgV5bXv8e9iCogoguDUKpooUWIc6DgkwSga5eIJzl55onHAkPgkxlwjJ+acHEXUm+R4EqMxE9FETIzDweMQB5zRGxNFEFQGEQeEFhBFmWwBwXX/eFfRxba7aRr23g31+zxPP937rXrrXTWuqrdqV5u7IyIixdSu2gGIiEj1KAmIiBSYkoCISIEpCYiIFJiSgIhIgXWodgAtsf3223ufPn2qHYaIyGZl0qRJ77p7r+bG2SySQJ8+fZg4cWK1wxAR2ayY2ZvrG0fdQSIiBaYkICJSYEoCIiIFtlncExDZFD766CPq6upYsWJFtUNpUufOnampqaFjx47VDkUKQklACqOuro5u3brRp08fzKza4XyCu7No0SLq6urYY489qh2OFIS6g6QwVqxYQc+ePdtkAgAwM3r27Nmmr1Rky6MkIIXSVhNApq3HJ1seJQERkQLTPQEptEWLFnHUUUcBsGDBAtq3b0+vXukLlhMmTKBTp06bvM3nn3+ehQsXMmjQoE0+bZENVdYkYGbdgRuAzwEOnAvMBG4H+gCzgdPc/f3WttF/xM0ATLr6GxsXrBRSz549mTJlCgAjR45k66235uKLL25x/TVr1tC+ffsNavP5559n6tSpSgLSJpS7O+haYJy7fxbYH5gBXAI85u57AY/FZ5E252tf+xr9+/enX79+3HDDDQCsXr2a7t278+Mf/5iDDz6YCRMmcO+999K3b18GDBjABRdcwAknnADA8uXLOfvsszn44IM58MAD+dvf/saHH37IqFGjuOWWWzjggAMYO3ZsNWdRpHxXAma2DXA4cDaAu68CVpnZ8cARMdoYYDzww3LFIdJaY8aMoUePHtTX11NbW8vJJ59Mt27dWLJkCQcddBBXXnkl9fX17L333jz99NPstttunHbaaWvrjxo1ikGDBnHTTTfx/vvvc8ghh/Diiy9y6aWXMnXqVH75y19Wce5EknJeCewJvAP8ycwmm9kNZtYV2MHd5wPE796NVTaz4WY20cwmvvPOO2UMU6Rx11xzDfvvvz+HHXYYdXV1vPbaawB06tSJE088EYDp06fTt29fdt99d8yMoUOHrq3/8MMPc9VVV3HAAQdw5JFHsmLFCubMmVOVeRFpSjnvCXQADgIucPdnzexaNqDrx91HA6MBamtrvTwhijTu0Ucf5amnnuKZZ56hS5cufPnLX177/H6XLl3WPsrp3vSm6e7cfffdfPrTn16n/Kmnnipf4CIbqJxXAnVAnbs/G5/HkpLC22a2E0D8XljGGERaZcmSJfTo0YMuXbowbdo0nnvuuUbH69evHzNnzmTu3Lm4O7fffvvaYcceeyzXXXfd2s+TJ08GoFu3bixbtqy8MyDSQmVLAu6+AJhrZn2j6ChgOnAvcFaUnQXcU64YRFrruOOOo76+nv33359Ro0ZxyCGHNDreVlttxfXXX8/RRx/NgAED2Hnnndl2220BuOyyy6ivr2e//fajX79+jBw5EoCBAwfywgsvcOCBB+rGsFRdub8ncAFwi5l1Al4HziElnjvMbBgwBzi1zDGItEh2kIb0IreHHnqo0fEWL168zuejjz6amTNn4u5861vfora2FoCuXbvyhz/84RP1e/XqpX+SJG1GWZOAu08BahsZdFQ52xWppN/+9rfccsstrFy5ktraWr75zW9WOySRFtM3hkU20ogRIxgxYkS1wxBplc3i3UEz6hbRf8TNa78dLCIim8ZmkQRERKQ8lARERApMSUBEpMB0Y1gKa1PfY2rpm2zHjRvHhRdeyJo1azjvvPO45BK9Q1GqR1cCIhW0Zs0avvOd7/Dggw8yffp0br31VqZPn17tsKTAlAREKmjChAl85jOfYc8996RTp06cfvrp3HOPvjQv1aMkIFJBb731FrvuuuvazzU1Nbz11ltVjEiKTklApIIae+uo/rm8VJOSgEgF1dTUMHfu3LWf6+rq2HnnnasYkRSdkoBIBX3hC19g1qxZvPHGG6xatYrbbruNIUOGVDssKTA9IiqF1dJHOjelDh06cP3113PssceyZs0azj33XPr161fxOEQySgIiFTZ48GAGDx5c7TBEAHUHiYgUmpKAiEiBKQmIiBSYkoCISIEpCYiIFJiSgIhIgekRUSmsOaP226TT2+3Sl9Y7zrnnnst9991H7969mTp16iZtX6Q1dCUgUkFnn30248aNq3YYImspCYhU0OGHH06PHj2qHYbIWkoCIiIFpiQgIlJgZb0xbGazgWXAGmC1u9eaWQ/gdqAPMBs4zd3fL2ccIiLSuEpcCRzp7ge4e218vgR4zN33Ah6LzyIiUgXVeET0eOCI+HsMMB74YRXikIJrySOdm9rQoUMZP3487777LjU1NVx++eUMGzas4nGIZMqdBBx42Mwc+L27jwZ2cPf5AO4+38x6N1bRzIYDwwE6detZ5jBFKuPWW2+tdggi6yh3EviSu8+LA/0jZvZySytGwhgN0HXHPT75j1lFRGSjlfWegLvPi98LgbuAg4G3zWwngPi9sJwxiIhI08qWBMysq5l1y/4GjgGmAvcCZ8VoZwH3lCsGkVLubfuisq3HJ1uecnYH7QDcZWZZO39193Fm9hxwh5kNA+YAp5YxBpG1OnfuzKJFi+jZsyexXbYp7s6iRYvo3LlztUORAilbEnD314H9GylfBBxVrnZFmlJTU0NdXR3vvPNOtUNpUufOnampqal2GFIgeouoFEbHjh3ZY489qh2GSJui10aIiBSYkoCISIEpCYiIFJiSgIhIgSkJiIgUmJKAiEiBKQmIiBSYkoCISIEpCYiIFJiSgIhIgSkJiIgUmJKAiEiBKQmIiBSYkoCISIEpCYiIFJiSgIhIgSkJiIgUmJKAiEiBKQmIiBSYkoCISIEpCYiIFJiSgIhIgSkJiIgUmJKAiEiBlT0JmFl7M5tsZvfF5z3M7Fkzm2Vmt5tZp3LHICIijavElcCFwIzc558B17j7XsD7wLAKxCAiIo0oaxIwsxrgOOCG+GzAQGBsjDIGOKGcMYiISNPKfSXwS+BfgY/jc09gsbuvjs91wC6NVTSz4WY20cwmrq5fVuYwRUSKqWxJwMz+BVjo7pPyxY2M6o3Vd/fR7l7r7rUdtupWlhhFRIquQxmn/SVgiJkNBjoD25CuDLqbWYe4GqgB5pUxBhERaUbZrgTc/UfuXuPufYDTgcfd/evAE8ApMdpZwD3likFERJpXje8J/BC4yMxeJd0juLEKMYiICOXtDlrL3ccD4+Pv14GDK9GuiIg0T98YFhEpsEImgf4jbqb/iJurHYaISNUVMgmIiEiiJCAiUmBKAiIiBaYkICJSYEoCIiIFpiQgIlJgSgIiIgWmJCAiUmAtSgJm9lhLykREZPPS7LuDzKwzsBWwvZltR8P/A9gG2LnMsYmISJmt7wVy3wK+TzrgT6IhCSwFfl3GuEREpAKaTQLufi1wrZld4O6/qlBMIiJSIS16lbS7/8rMvgj0yddx9y3qLWz5l8pNuvobVYxERKQyWpQEzOzPwKeBKcCaKHZgi0oCIiJF09J/KlML7Ovujf5TeBER2Ty19HsCU4EdyxmIiIhUXkuvBLYHppvZBGBlVujuQ8oSlYiIVERLk8DIcgYhIiLV0dKng54sdyAiIlJ5LX06aBnpaSCATkBH4AN336ZcgYmISPm19EqgW/6zmZ0AHFyWiEREpGJa9RZRd78bGLiJYxERkQpraXfQSbmP7UjfG9B3BkRENnMtfTroa7m/VwOzgeObqxBvIH0K+FS0M9bdLzOzPYDbgB7A88CZ7r5qA+NeL70CQkRk/Vp6T+CcVkx7JTDQ3ZebWUfg72b2IHARcI2732ZmvwOGAb9txfRFRGQjtfSfytSY2V1mttDM3jazO82sprk6niyPjx3jx0n3EsZG+RjghFbGLiIiG6mlN4b/BNxL+r8CuwB/i7JmmVl7M5sCLAQeAV4DFrv76hilLqbXWN3hZjbRzCaurl/WwjBFRGRDtDQJ9HL3P7n76vi5Cei1vkruvsbdDwBqSI+U7tPYaE3UHe3ute5e22Grbo2NIiIiG6mlSeBdMzsjzuzbm9kZwKKWNuLui4HxwKFAdzPL7kXUAPM2JGAREdl0WpoEzgVOAxYA84FTgGZvFptZLzPrHn93AY4GZgBPRH2As4B7NjxsERHZFFr6iOgVwFnu/j6AmfUA/ouUHJqyEzDGzNqTks0d7n6fmU0HbjOzK4HJwI2tjl5ERDZKS5PA57MEAODu75nZgc1VcPcXgU+M4+6vo1dOiIi0CS3tDmpnZttlH+JKoKUJRERE2qiWHsh/DvzDzMaSnuY5DbiqbFGJiEhFtPQbwzeb2UTSF70MOMndp5c1MhERKbsWd+nEQV8HfhGRLUirXiUtIiJbBiUBEZECUxIQESkwJQERkQJTEhARKTAlARGRAlMSEBEpMCWBFuo/4uZ1/m9xueqIiFSSkoCISIEpCYiIFJjeBLqR8t09k67+RhUjERHZcLoSEBEpMCUBEZECUxIQESkwJQERkQJTEhARKTAlARGRAlMSEBEpMCUBEZECUxIQESkwJQERkQIrWxIws13N7Akzm2Fm08zswijvYWaPmNms+L1duWIQEZHmlfNKYDXwA3ffBzgU+I6Z7QtcAjzm7nsBj8VnERGpgrIlAXef7+7Px9/LgBnALsDxwJgYbQxwQrliEBGR5lXknoCZ9QEOBJ4FdnD3+ZASBdC7iTrDzWyimU1cXb+sEmGKiBRO2ZOAmW0N3Al8392XtrSeu49291p3r+2wVbfyBSgiUmBlTQJm1pGUAG5x9/+J4rfNbKcYvhOwsJwxiIhI08r5dJABNwIz3P0XuUH3AmfF32cB95QrBhERaV45/7PYl4AzgZfMbEqU/RvwU+AOMxsGzAFOLWMMIiLSjLIlAXf/O2BNDD6qXO2KiEjL6RvDIiIFpiQgIlJgSgIiIgWmJCAiUmBKAiIiBaYkICJSYEoCIiIFVs4vi0kT+o+4ee3fk67+RhUjEZGi05WAiEiBKQmIiBSYkoCISIHpnkAbonsFIlJpuhIQESkwJQERkQJTEmjCnFH7MWfUftUOQ0SkrJQEREQKTElARKTAlARERApMj4hWWXbfYbdLX9qg8TekTjlV47HWDV1mItI0XQmIiBSYkoCISIEpCYiIFJiSgIhIgSkJiIgU2BbzdFBbe2pmU8uewrmrW5UDaYOyZdPc00l6okikcWW7EjCzP5rZQjObmivrYWaPmNms+L1dudoXEZH1K2d30E3AoJKyS4DH3H0v4LH4LCIiVVK2JODuTwHvlRQfD4yJv8cAJ5SrfRERWb9K3xPYwd3nA7j7fDPr3dSIZjYcGA7QqVvPCoVXHtW+X1Ha/sZ+y3dz/5aziDRos08Huftod69199oOW+luqIhIOVQ6CbxtZjsBxO+FFW5fRERyKt0ddC9wFvDT+H1Phdvfom0uj0G2pjsqX0ePyYpsOuV8RPRW4J9AXzOrM7NhpIP/V81sFvDV+CwiIlVStisBdx/axKCjytWmiIhsmDZ7Y1hERMpvi3ltRGu05tHF5upsLn3y0jJan1IEuhIQESkwJQERkQIrRHfQlnxZ39yjk5vjm0db8kbQpmyKbya35FHUtvwN6C15W5fy0JWAiEiBKQmIiBRYIbqDNkdtrcuhNd0kG9MdtTnOv8jmSFcCIiIFpiQgIlJgSgIiIgW22d0TaGt9xVuahn78q9eWbcnLuS1vT0097tmWY5bNj64EREQKTElARKTANrvuINm09A3TTWdT/y/nTamcLz5sbj435hvgm0K1298c6EpARKTAlARERApMSUBEpMB0T0CqqlqPO7aVN6w2dx+hGm+FLdd9jA1Zz9Xux692+5WmKwERkQJTEhARKTB1B8kWq1Jv/mxNN021u6M2Ztls6i68jf1m9LrzUr5vujfWTtZGa7qQ2kq3k64EREQKTElARKTA1B0kUgHl/GZ2U107G9sd1pp/EtTS+WxNbM11x2yoSj0FtbHt5Jdnub6ZXZUrATMbZGYzzexVM7ukGjGIiEgVkoCZtQd+DfwvYF9gqJntW+k4RESkOlcCBwOvuvvr7r4KuA04vgpxiIgUnrl7ZRs0OwUY5O7nxeczgUPc/bsl4w0HhsfHvsDM+Ht74N1GJt1U+ZZWp9rtt6ZOtduvVJ1qt1+pOtVuvzV1qt1+peqUlu/u7r2amHbi7hX9AU4Fbsh9PhP41QbUn7gh5VtanWq3X5SYNZ9tt/2ixLyp57Opn2p0B9UBu+Y+1wDzqhCHiEjhVSMJPAfsZWZ7mFkn4HTg3irEISJSeBX/noC7rzaz7wIPAe2BP7r7tA2YxOgNLN/S6lS7/dbUqXb7lapT7fYrVafa7bemTrXbr1Sd5qbVqIrfGBYRkbZDr40QESkwJQERkSLb0MeJKvVDeoLoCWAGMA24MMpPJX1nwIE3Sob9DvgAWAEsBS6J8itiOsuA5VH/wpJ23o5p/luUjwQWRJ0VwJsldV4BVsawx6L89ohnGbAK+DBX51hgSYz/IfDzKP9CjP9h/P5JlO8BTIjxFwPTgctj2Pej3IGXc+W3RFz1wHsRSzbspij/MOL4v7ll3Tnmf01JnT/HPH4YP7/LjT8vhq0EHozy/we8EOvgo1gH2bQGRfmH8fu6KB8IPA9MBW4GJgP35ZbBs8As4H3g/ij/LvBqzP+LufFviXU7FfhTybRujNhejGk9WLK9/Yq0beTr3ETaxqbEsnsqyg24Kpb1DNITb/fllsGU+PkIWBDlR8V8Tol2Hi+Z/1UR1xTiMT+gB/BITGcZ8FJu2KlRx2Oes/KrSdvEqljP+TpX0LBdLgVeyM3/bGB+TG9Kbh94K1fnNXKPIAKLaNg+svm8PeZhVa5e1v4BufHrgelRvj/wT9K2Ny+3XA+LZfBEzP8HMa+HxfzPiHhn58bP5n9qxJ6f1hVRvjimNQs4LDc/P47pzcrVGRkxLSbtc3OyOsAFMe6SWBZZndtjuS+Odbciyg8gPRizOJbBG1G+P2m7y/bNF2P9fJ/05dplsdyW5cqz+ZwXZdOBW0n7Zn7/2H69x9pqH+ybSQI7AQfF391iZe4L7AN8GZgI1JYMOx34QtS5hnQg3BfYJpse8D3SASGrsxPpFRYPxQp+NcpHApc3EcPJpIPTp6L8NWDffNzAz4Erc3XGA9+NcU4m7QT7xkYxKMq/SdpwDwXuiPnZmpTcvhNtHgocGHVnAzvmygeTDlBbxwaRr7MNsHW088uY10Pjc22MvxzomKtzE3BGjJMvPwf4K+lKsiMwKTetrP07Y7yszitA/9xB/B3gi8BcYO8ofxh4hoYDarYMLiLtbC9F+YFAn1i/d+bGz+bfSAfWCblh28Tvi0g764zctlZLQ8L7K+smgVOiTr78HFLCahfD1saQm+ZFsYwnx+dXSNvuRbHO50b9ucDesS6vBoblpvGfwCUxbBTws9ywfUjJ52mgNld+DOmBj9nAdSV1tony7Un7we9yw+YCj5NOdrbPJYGLszol83ck6aC1c3zuXTJ8Nun1MJfmyh4mnWxsH+tqfJQ/B3wFGENK3lcAnYDusQwmAefFsrg6yvcB7iIlhdrc+Nn8jwEeBH6WG7ZNlJ8X8z8a6B4x7Era9xZFfFmdkaRjzXkxXlZ+JPAoabs5D+idDYvxsnZ+TjqOdI/5fzTKBwNPRvlzwFei3rmk48YCYPdsG4hhPyLto7vHfO5GSiQ/j/m8Azibhv3jE+utsZ822x3k7vPd/fn4exlpx93F3We4+99JC6N02G3u/lxM4ilStt/F3ZfmpteVdIaS1ZkPDAP+FfiYdLDZJaaxrLEYgP8N/Lu7r4zyqVmdmN5k4DTShpDVWUk68EHaWN6L8r6kBAQpUfQgZfCBwFh3Xx7TOYF0wHV3n+zu06NOx1z5A54sJx0Ad8sNW+ruy83MYhm0Bzze5XQ16eC0zvTi84pGys8n7dwfR5ll43vaWi3ifyBXx2l4Gq0H6apjDbDS3V8xs5ooN4CIcyDpDPE44Fpgh2hjMrAa6ELaCYnyB6L9XUiJ6JXcsKXRxnGkdUy0k83/NbFMbmBdPaJOvvx80kF55xj263yFaGcIsB3poEfM/6dj/CmxXHtm8x/jPEk6QcgcT1r3kM4uT8jNz4xYButw94fdPSufSPoeztplkBu1Kw3rOJvPy0vKmnM+6ax1VUx7YSPjnEA6uVgbArF+gW1p+H5QX9I+czhwKXCyu69y98XAiUAv0onbGGBIlL9FOrOeH+2vcvfF7v4wsFVMazRQk5sWUX5jzP+aXPmv4vey/PRIJ3p7RJ18+fmkJPtl4EZ3X5gNM7Ntcu2cBvwl6rQDPh/l2wJvRXlf0vEK0pXfmcBr7v4m624DbwDt3f3NmM81pH1qCimJbQXMi+PD7EbWR+PWlyXawg8pq80hzuaibDzpDOATw2L4o6SvT2dngFeRznamks7U55DODIYA18Y4dfGzDekMYDbp0uyPsfKyOlNIO8yzpDPXBSWxHU7aAfvk6uwTf8+N8d+K8n+QNvQpNHSvbE96vxKkA9O0WOH5s7r2pB1web48hn2KdKVRX1LnJtLl6WoauqMuBP5PTG9Nfnox/kzSGd8q4L+ifBHp0rk+pjW6pP2zSJe8+WkNiHqrSMn2GtIB4c1Yj2NJB4zXgfuyZRDl/UmX/0tzbYwlHUSO55Nn4XeSku8F+WGxPheRDjgPlMz/2JifI1j3SmAZKWn8d67OIuDfSYn8aeDrJe2MJR3MnsxNa0Cs27cjjnEl8/8GsDCW9fCoszh+v0G6slmdDcuVZycnw0uWwRuxDt4oqbOYhm6ai6JsCOmA/nzEmJWPjFhXkfalKbnYppC6rz6IGK4qaX9eDJuUq7NPzEPWVZSd4f6D1MUxIX7WkJJu15j2hFgXkyO+rqQEMIG0L72cjR/Ty4bNjfhvyNXJ4voQ+EuUDyF1JU4gbbMv5ur8Ltp8L9b7zVE+JYYtjxhmlrQzgXQlUp8rPzGm9UHM/21R/g/g+Ij9ItI++t38NhB//xGoL1nOF8YyXQrcUjJsNptzd1BuRraODemkkvLxpB2rsWGXxwZ6UiPTuyw2hJNImfNZUlbeOlbQWTHeDqQDYzvSJdm72fRIieS6qDODtGNbro3fki7dJuXqXEc6y9uadKB7Ico/S7pMnAT8JDaAAUQSiHF2JSWCJ4DPlazkPRsp/wOpy6d7I8Oys90ZpGT1d6BDDFuer0Pq2jJSUvlrxP25GO8HUecM0oEl38aDMa/5af0P6R1RAP8R6+BzpD7RabEMryQdcO8jnf3NA34TddYmAeBfgN/E/K+TBGLY9Jj/I2g4CGd12gN3k3binWP+h8Sw5SV1zozyT5EO2jNzy+mGGHYS696XyNp5kJQIsvJ/kK7sIG0fc+Lvw0j3ESbH/L9EundxOA1JIOtyWZwNy8pJ+8FX8+Ux7Kek7pLepXXid9blcDhpH+gb5XNI2/fhNOwDu5BOov6ai20q6YzWSPe7Vpa0Pwb4Qb590j6QdaucRzrAH07aB54hXSmMJh1sryV1Cy0jHeSybWdFlNdG+aT4+1rgihinlpRIxkd815bUOYS0fz5Dugp8ltQdtZq0zW2fq3NMlB8ay2AaDfcWboth55CSbWk7d8YyyMpvi7gOIV0hzIny/DFgFOkkaYd8EiD1HrzLuklhO1JX9P2kK+67ie7bLSYJxIw9RJyZlAx7ktRVcFFJ+bmkneWHTUzvSWB+fN6PdPY1m3RmsCZWzI5N1YmycaQbfQ+RMvdrQK8Y1oF0QBufj410ptUxV2dpI/HtTboSGRErPDs4Hxb1LgMuLl3J+fL4+26gXe7zxSXtfIV05nIZ6UAwO34+Jp19N1bniKhzMenMq0+UG2nHzNrvSdqJO+faH0G6vM2mtVss96zOT2K+F9Bw0/CW+F1Hwxn8GtLZWzb+6ljW9aRLbkgH1A+jzoJsWK5OflrvxziLY1oevxur816u/ZdJB/ps2McldebFuFn790db2fgLo85fSpbxMaR+3ZGxnGcCO8WwneLzSNbdBsaTDjpry0lXYv8EtorP69SJst0jjv+gYR+YHfO/hLihnxu/D+nAl8U2DjgiN/w94LKSfaAm335M13LbzcpczDtG+3uTzqIHxHJ7FZibWwZzojwbP5v/ATQ8OPA90jaZzf+AfJ3c/L8OPBbzPzfmfXW0cXwjdfpEnftj/k/KDXuNdAKQj+1tUndc1v7S3PhGuiK4v2Q5nw8syX2eGfN9fMzrzNyw66ONbD6/QZw0bUgSaLP3BKJP+EbSDbxfNDKsL/BGfpiZDQJ+Adzu7j/Lle+Vm97HpEt43P0l0tnOU8DvSRvCQe6+wMx2ytWBlAgyd0c7M0hnrVmWBjiahics8nHPi3FnkM6MZkVs+5hZdzNrRzpg1sc4TwDnmFl30k79QEz7ZTPrFeWQngbIys8j9Tl/290/NrMuMWymmR0c7RjpsrQrMMnddyQ9oXRAtL1fbnr9cnVOJl05vUxKSMdF+18lXb28HJ/PAR5y9xW59mcA3c2sf4wzmHSF9bKZ9Xb3H5H6y6cB/0Z6cubrpB3nYnfvE+tsuruf4e4/cvca0kF1eIx/Rsx/e6BH1DmddLPzTFK/bQ2pf/dR0s64nbvv6O7d3b0Daac8Opse6QmmrM7jpO3tjFj/E2PY2aR7D49nsZHO5v6Sa/94UlIYGHHdBLwdMfc2s65m1hP4YQw7hnTAvRc4z8y65baBY4CpUSf7n1ydc+WDSDdQh7p7vZl1zQ37fK7OKbEOnov52y9ie4t04Pln7ANZOyfGesxie4D0xBdmtn9sT8/GtI8DZrl7Xb59Uv/9oNw28FHE1dvdF5D2v/8kdbMcRbqiuwtYbWZ9YxnMje0gG79LTO8oYHrM/7dp6CcnN61uwNyY1hBSonje3Xu7+66kxLmA1KuoweQAAAPUSURBVF18YNSxXJ0TiaeaYhs4IIYdSzoG1OZiWw686e51ufbfApbGtAaSkuJ0M+sdy7Ed6d7kWBrcG/M9lHRP8Z4Yd1As5/fjs0U7M9hQ68sS1foh3XBx0qV29sjd4FgRC2NY9hhiNqwuyrNHGt+N8jtJGdxj/Gm5Ovl2VpEuxweTbji+FsOWkDbirM4RUb6CtFG8CgyOuB9oIu7zc7Flj7oNJt3VX0HDjeNLYzp7RiwrSWeq03LDfkrD44EfkQ7mkM5i5tLwKOjbpC6JdjQ8gpY9cnpVbll/PoavifnM2nm2pM6VUf6lWCbZWfuvc9N6LpbHiyXT+n5uvXwAXBPlV5M23JkxzhE0dKHsSTorfJV0FpT1yX+PhiuBd2noWlkd6yxb7n8kJd52pCTyUsT0CDCukW2utDvo8ZI62aOw3UkJ6iXSgWMY63ZJjScd7PLTOpGGrp4pNDwienXM3wrSQWIa6aEDSFdVT9PweODLuWHfpuH+ymrSQZeY1rzcsn4vV2dcrnwZDfdr9oy4XohpXhXlf442s0cXZ+am1Zd0AMqmd2Nu/sfG+nmhZH5Oo2HbXLvdkPq1XyHdH5lP2nbuJnV39CRthysi5vuj/ETS9v0xaR94O8pfJe0DM6ONRblp3RnD60nHgXHAdrm4s0dYp+Xq/Jl0wlYfy+DBKO9ESvSvkrbn17I6Ma17Y37y8/JlUjLI7tc9EeXZ/M+KZbNtLqaeMV7WvdUjt56z+4srYj3/mdR1md8/5pF7a3NjP3pthIhIgbXZ7iARESk/JQERkQJTEhARKTAlARGRAlMSEBEpMCUBkfUwsxPNzM3ss9WORWRTUxIQWb+hpNdLnF7tQEQ2NSUBkWaY2dakL8cNI5KAmbUzs9+Y2TQzu8/MHjCzU2JYfzN70swmmdlDZrZTFcMXWS8lAZHmnUD6dvErwHtmdhDpnTF9SK/YOI/0bifMrCPplcSnuHt/0jeWr6pG0CIt1WH9o4gU2lDSG0khvQVyKOlFgP/t6f8pLDCzJ2J4X9KbUR9Jr3KhPfG+e5G2SklApAnxUreBwOfMzIl/xEN6qVmjVYBp7n5YhUIU2WjqDhJp2inAze6+u7v3iTdNvkF6ad3JcW9gB9KL4iC9tKyXma3tHjKzftUIXKSllAREmjaUT57130n6Zy51pLeL/p70lssl7r6KlDh+ZmbZ20K/WLlwRTac3iIq0gpmtrWn/9nck/S66y/Fe+RFNiu6JyDSOvfFP/bpRPq3hkoAslnSlYCISIHpnoCISIEpCYiIFJiSgIhIgSkJiIgUmJKAiEiB/X9rfV3+qhDfWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Age' , hue = 'Target', data =data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pregnants</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood_pressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serum_insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              pregnants  Plasma_glucose_concentration  \\\n",
       "pregnants                      1.000000                      0.129459   \n",
       "Plasma_glucose_concentration   0.129459                      1.000000   \n",
       "blood_pressure                 0.141282                      0.152590   \n",
       "Triceps_skin_fold_thickness   -0.081672                      0.057328   \n",
       "serum_insulin                 -0.073535                      0.331357   \n",
       "BMI                            0.017683                      0.221071   \n",
       "Diabetes_pedigree_function    -0.033523                      0.137337   \n",
       "Age                            0.544341                      0.263514   \n",
       "Target                         0.221898                      0.466581   \n",
       "\n",
       "                              blood_pressure  Triceps_skin_fold_thickness  \\\n",
       "pregnants                           0.141282                    -0.081672   \n",
       "Plasma_glucose_concentration        0.152590                     0.057328   \n",
       "blood_pressure                      1.000000                     0.207371   \n",
       "Triceps_skin_fold_thickness         0.207371                     1.000000   \n",
       "serum_insulin                       0.088933                     0.436783   \n",
       "BMI                                 0.281805                     0.392573   \n",
       "Diabetes_pedigree_function          0.041265                     0.183928   \n",
       "Age                                 0.239528                    -0.113970   \n",
       "Target                              0.065068                     0.074752   \n",
       "\n",
       "                              serum_insulin       BMI  \\\n",
       "pregnants                         -0.073535  0.017683   \n",
       "Plasma_glucose_concentration       0.331357  0.221071   \n",
       "blood_pressure                     0.088933  0.281805   \n",
       "Triceps_skin_fold_thickness        0.436783  0.392573   \n",
       "serum_insulin                      1.000000  0.197859   \n",
       "BMI                                0.197859  1.000000   \n",
       "Diabetes_pedigree_function         0.185071  0.140647   \n",
       "Age                               -0.042163  0.036242   \n",
       "Target                             0.130548  0.292695   \n",
       "\n",
       "                              Diabetes_pedigree_function       Age    Target  \n",
       "pregnants                                      -0.033523  0.544341  0.221898  \n",
       "Plasma_glucose_concentration                    0.137337  0.263514  0.466581  \n",
       "blood_pressure                                  0.041265  0.239528  0.065068  \n",
       "Triceps_skin_fold_thickness                     0.183928 -0.113970  0.074752  \n",
       "serum_insulin                                   0.185071 -0.042163  0.130548  \n",
       "BMI                                             0.140647  0.036242  0.292695  \n",
       "Diabetes_pedigree_function                      1.000000  0.033561  0.173844  \n",
       "Age                                             0.033561  1.000000  0.238356  \n",
       "Target                                          0.173844  0.238356  1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#相关系数\n",
    "cor_data = data.corr()\n",
    "sns.heatmap(cor_data)\n",
    "cor_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'info'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-70c3197cf6e0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[0mtrain_sx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_sx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_sy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_sy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m26\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#分离训练数据和测试数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[0mtrain_mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m26\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#分离训练数据和测试数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 31\u001b[1;33m \u001b[0mtrain_sy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\ProgramFiles\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5272\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5273\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5274\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5276\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'info'"
     ]
    }
   ],
   "source": [
    "#特征工程\n",
    "#onehot\n",
    "pregnants = data.pregnants.astype('object')\n",
    "pregnants = pd.get_dummies(pregnants , prefix='p+number')\n",
    "#缺失值检查\n",
    "data_depregnants = data.drop('pregnants',axis=1) #去掉pregnants特征\n",
    "Nan_col = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "#缺失值填充\n",
    "data_depregnants[Nan_col] = data_depregnants[Nan_col].replace(0,np.NaN)\n",
    "median = data_depregnants[Nan_col].median()#取中值\n",
    "data_depregnants[Nan_col] = data_depregnants[Nan_col].fillna(median)\n",
    "#标准化\n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_s = StandardScaler()\n",
    "train_m = MinMaxScaler()\n",
    "\n",
    "feat_col = data_depregnants.columns\n",
    "data_s = train_s.fit_transform(data_depregnants)#对数据进行标准化处理\n",
    "data_m = train_m.fit_transform(data_depregnants)\n",
    "data_s = pd.DataFrame(data = data_s, columns = feat_col, index = data_depregnants.index)\n",
    "data_m = pd.DataFrame(data = data_m, columns = feat_col, index = data_depregnants.index)\n",
    "s_onehot = pd.concat([data_s,pregnants],axis=1,ignore_index=False)#加入独热后的pregnants数据\n",
    "m_onehot = pd.concat([data_m,pregnants],axis=1,ignore_index=False)\n",
    "s_x = s_onehot.drop('Target',axis=1) #分离参数特征\n",
    "s_y = s_onehot.Target #分离目标特征\n",
    "m_x = m_onehot.drop('Target',axis=1)\n",
    "m_y = m_onehot.Target\n",
    "train_sx, test_sx, train_sy, test_sy = train_test_split(s_x, s_y,test_size=0.2,random_state=26)#分离训练数据和测试数据\n",
    "train_mx, test_mx, train_my, test_my = train_test_split(m_x, m_y,test_size=0.2,random_state=26)#分离训练数据和测试数据\n",
    "train_sy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[0.0001, 0.001, 0.01, 0.1, 1.1, 100, 1000, 10000],\n",
       "                     class_weight=None, cv=5, dual=False, fit_intercept=True,\n",
       "                     intercept_scaling=1.0, l1_ratios=None, max_iter=100,\n",
       "                     multi_class='ovr', n_jobs=None, penalty='l2',\n",
       "                     random_state=None, refit=True, scoring='accuracy',\n",
       "                     solver='liblinear', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#-log似然损失\n",
    "#建立学习器\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "#超参数调优\n",
    "Cs = [0.0001,0.001,0.01,0.1,1.10,100,1000,10000]\n",
    "#-log损失 L1 正则\n",
    "lr_l1 = LogisticRegressionCV(Cs=Cs, cv=5, scoring='neg_log_loss',penalty='l1',solver='liblinear',multi_class='ovr')\n",
    "#-log损失 L2 正则\n",
    "lr_l2 = LogisticRegressionCV(Cs=Cs, cv=5, scoring='neg_log_loss',penalty='l2',solver='liblinear',multi_class='ovr')\n",
    "#正确率 L1 正则\n",
    "lr_as_l1 = LogisticRegressionCV(Cs=Cs, cv=5, scoring='accuracy',penalty='l1',solver='liblinear',multi_class='ovr')\n",
    "#正确率 L2 正则\n",
    "lr_as_l2 =  LogisticRegressionCV(Cs=Cs, cv=5, scoring='accuracy',penalty='l2',solver='liblinear',multi_class='ovr')\n",
    "#训练模型\n",
    "lr_l1.fit(train_sx,train_sy.astype('int'))\n",
    "lr_l2.fit(train_sx,train_sy.astype('int'))\n",
    "lr_as_l1.fit(train_sx,train_sy.astype('int'))\n",
    "lr_as_l2.fit(train_sx,train_sy.astype('int'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'logloss')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l1_score_lr = -np.mean(lr_l1.scores_[1],axis=0) # -log L1\n",
    "l2_score_lr = -np.mean(lr_l2.scores_[1],axis=0) #-log L2\n",
    "l1_score_as = np.mean(lr_as_l1.scores_[1],axis=0) #正确率 L1\n",
    "l2_score_as = np.mean(lr_as_l2.scores_[1],axis=0) #正确率 L2\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(np.log10(Cs),l1_score_lr,label='L1')\n",
    "plt.plot(np.log10(Cs),l2_score_lr,label='L2')\n",
    "plt.legend()\n",
    "plt.xlabel('logC')\n",
    "plt.ylabel('logloss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[0.6504065 , 0.6504065 , 0.64227642, 0.69105691, 0.70731707,\n",
       "         0.70731707, 0.70731707, 0.70731707],\n",
       "        [0.6504065 , 0.6504065 , 0.69105691, 0.76422764, 0.76422764,\n",
       "         0.75609756, 0.75609756, 0.75609756],\n",
       "        [0.64227642, 0.64227642, 0.7398374 , 0.78861789, 0.77235772,\n",
       "         0.75609756, 0.75609756, 0.75609756],\n",
       "        [0.64227642, 0.64227642, 0.75609756, 0.76422764, 0.75609756,\n",
       "         0.77235772, 0.77235772, 0.77235772],\n",
       "        [0.64754098, 0.64754098, 0.66393443, 0.78688525, 0.7704918 ,\n",
       "         0.75409836, 0.75409836, 0.75409836]])}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_as_l1.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'accuracy')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(np.log10(Cs),l1_score_as,label='L1')\n",
    "plt.plot(np.log10(Cs),l2_score_as,label='L2')\n",
    "plt.legend()\n",
    "plt.xlabel('logC')\n",
    "plt.ylabel('accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#最优超参数 为 0.1 \n",
    "#在正确率评估下，L2正则的效果略优于L1\n",
    "#-log似然损失下，L1和L2正则测试效果一致"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
